Sharp Images Detection for Microscope Pollen Slides Observation

Aysha Kadaikar (1, 2) , Maria Trocan (2) , Frédéric Amiel (2) , Patricia Conde-Cespedes (2) , Benjamin Guinot (1) , Roland Sarda Estève (3) , Dominique Baisnee (3) , Gilles Oliver (4)

1 LAERO - Laboratoire d'aérologie
2 ISEP - Institut Supérieur d'Electronique de Paris
3 CAE - Chimie Atmosphérique Expérimentale
4 RNSA - Réseau National de Surveillance Aérobiologique

In this paper, a new preprocessing algorithm to qualify images of different pollen grains for further processing is proposed. This algorithm provides a score related to the sharpness of the image and will be used to automatically adjust the focal length of a microscope that magnifies the image. The obtained score has been compared to four quality metrics generally used to estimate the clarity of an image and to a reference made by a human. The results of the simulations show that the proposed algorithm combines better performance with low complexity on the set of images.

Domaines: Sciences de l'environnement; Ingénierie de l'environnement

Aysha Kadaikar, Maria Trocan, Frédéric Amiel, Patricia Conde-Cespedes, Benjamin Guinot, et al.. Sharp Images Detection for Microscope Pollen Slides Observation. Asian Conference on Intelligent Information and Database Systems : Intelligent Information and Database Systems, Apr 2019, Yogyakarta, Indonesia. pp.661-671, ⟨10.1007/978-3-030-14799-0_57⟩. ⟨hal-02352547⟩